Modeling-Based Design of Memristive Devices for Brain-Inspired Computing

Abstract

Resistive switching random access memory (RRAM) has emerged for non-volatile memory application with the features of simple structure, low cost, high density, high speed, low power, and CMOS compatibility. In recent years, RRAM technology has made significant progress in brain-inspired computing paradigms by exploiting its unique physical characteristics, which attempts to eliminate the energy-intensive and time-consuming data transfer between the processing unit and the memory unit. The design of RRAM-based computing paradigms, however, requires a detailed description of the dominant physical effects correlated with the resistive switching processes to realize the interaction and optimization between devices and algorithms or architectures. This work provides an overview of the current progress on device-level resistive switching behaviors with detailed insights into the physical effects in the resistive switching layer and the multifunctional assistant layer. Then the circuit-level physics-based compact models will be reviewed in terms of typical binary RRAM and the emerging analog synaptic RRAM, which act as an interface between the device and circuit design. After that, the interaction between device and system performances will finally be addressed by reviewing the specific applications of brain-inspired computing systems including neuromorphic computing, in-memory logic, and stochastic computing.